Data-driven Design of Catalytic Materials in Methane Oxidation Based on a Site Isolation Concept

Abstract

We present a general data-driven strategy for the search for catalytic materials, focusing particularly on materials useful for the conversion of natural gas (methane) to ethane and ethylene (OCM: oxidative coupling of methane reaction). OCM facilitates the transportation of natural gas and provides a way to synthesize higher-value chemicals. Our strategy is based on consistent experimental measurements and includes ab initio thermodynamics calculations and active screening. Based on our experiments, which showed a volcano-type dependence of the performance on the stability of formed carbonates attributed to the site isolation concept, we developed a method for efficient and inexpensive DFT calculations of the formation energies of carbonates with a prediction accuracy of 0.2 eV based on the Boltzmann distribution of surface terminations. This method was implemented into a high-throughput screening scheme, which includes both general requirements for catalyst candidates and an actively performed artificial intelligence part. Guided by theoretical predictions, we have performed experimental validation of some of the candidates obtained during the screening which showed successful reproduction of the initial volcano dependence. Predicted in this way, materials were found to show in general comparable performance to well-known standard OCM catalysts, or even higher yields specifically at temperatures between 700 and 800 °C.